Sampling SVD singular vectors for Distributional Semantics Models
Project description
entropix
Generate count-based Distributional Semantic Models by sampling SVD singular vectors instead of using top components.
Install
pip install entropix
or, after a git clone:
python3 setup.py install
Use
Sequential mode
entropix sample \ --model /abs/path/to/dense/numpy/model.npy \ --vocab /abs/path/to/corresponding/model.vocab \ --dataset dataset_to_optimize_on \ # men, simlex or simverb --shuffle \ --mode seq \ --kfold-size .2 \ # size of kfold, between 0 and .5 --metric pearson \ # spr(spearman), pearson, rmse or both (spr+rmse) --num-threads 5
Limit mode
entropix sample \ --model /abs/path/to/dense/numpy/model.npy \ --vocab /abs/path/to/corresponding/model.vocab \ --dataset dataset_to_optimize_on \ # men, simlex or simverb --mode limit \ --metric pearson \ --limit 10 # number of dimensions to sample
Project details
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Filename, size | File type | Python version | Upload date | Hashes |
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Filename, size entropix-2.0.1.tar.gz (7.9 kB) | File type Source | Python version None | Upload date | Hashes View |